Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

© Timothy Masters 2018

Timothy MastersAssessing and Improving Prediction and Classificationhttps://doi.org/10.1007/978-1-4842-3336-8_7

7. Combining Classification Models

Timothy Masters¹ 

(1)

Ithaca, New York, USA

 

• The Majority Rule

• The Borda Count

• The Average and Product Rules

• The MaxMax and MaxMin Rules

• The Intersection and Union Rules

• Logistic Regression

• Model Selection by Local Accuracy

• Maximizing the Fuzzy Integral

• Pairwise Coupling

Chapter 6 discussed methods for combining several models that are designed to make numeric predictions. For classification models that base their decisions on numeric predictions, the methods of that chapter are often a good choice. However, some models are inherently strict classifiers in that they produce a class decision ...